Overview

Dataset statistics

Number of variables19
Number of observations1296675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory468.5 MiB
Average record size in memory378.9 B

Variable types

DateTime1
Numeric12
Text2
Categorical4

Alerts

lat is highly overall correlated with merch_latHigh correlation
long is highly overall correlated with merch_longHigh correlation
merch_lat is highly overall correlated with latHigh correlation
merch_long is highly overall correlated with longHigh correlation
month is highly overall correlated with yearHigh correlation
year is highly overall correlated with monthHigh correlation
is_fraud is highly imbalanced (94.9%)Imbalance
amt is highly skewed (γ1 = 42.27787379)Skewed
hour has 42502 (3.3%) zerosZeros
dayofweek has 254282 (19.6%) zerosZeros

Reproduction

Analysis started2025-12-02 02:46:14.039195
Analysis finished2025-12-02 02:47:59.786773
Duration1 minute and 45.75 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct1274791
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum2019-01-01 00:00:18
Maximum2020-06-21 12:13:37
Invalid dates0
Invalid dates (%)0.0%
2025-12-02T08:17:59.874708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:59.996338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cc_num
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1719204 × 1017
Minimum6.0416207 × 1010
Maximum4.9923464 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-12-02T08:18:00.155148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.0416207 × 1010
5-th percentile6.3048488 × 1011
Q11.8004295 × 1014
median3.5214173 × 1015
Q34.6422555 × 1015
95-th percentile4.497914 × 1018
Maximum4.9923464 × 1018
Range4.9923463 × 1018
Interquartile range (IQR)4.4622125 × 1015

Descriptive statistics

Standard deviation1.3088064 × 1018
Coefficient of variation (CV)3.1371798
Kurtosis6.1799499
Mean4.1719204 × 1017
Median Absolute Deviation (MAD)3.0764709 × 1015
Skewness2.851879
Sum-6.7255419 × 1018
Variance1.7129743 × 1036
MonotonicityNot monotonic
2025-12-02T08:18:00.327948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.512828415 × 10183123
 
0.2%
5.713652351 × 10113123
 
0.2%
3.672269902 × 10133119
 
0.2%
2.131124026 × 10143117
 
0.2%
3.54510934 × 10153113
 
0.2%
6.534628261 × 10153112
 
0.2%
6.011367958 × 10153110
 
0.2%
2.720433096 × 10153107
 
0.2%
6.011438889 × 10153106
 
0.2%
6.011109737 × 10153101
 
0.2%
Other values (973)1265544
97.6%
ValueCountFrequency (%)
6.041620718 × 10101518
0.1%
6.042292873 × 10101531
0.1%
6.042309813 × 1010510
 
< 0.1%
6.042785159 × 1010528
 
< 0.1%
6.048700208 × 1010496
 
< 0.1%
6.04905963 × 10101010
0.1%
6.049559311 × 1010518
 
< 0.1%
5.018029536 × 10111559
0.1%
5.018181333 × 10118
 
< 0.1%
5.018282048 × 1011515
 
< 0.1%
ValueCountFrequency (%)
4.992346398 × 10182059
0.2%
4.989847571 × 10181007
 
0.1%
4.980323468 × 1018532
 
< 0.1%
4.973530368 × 10181040
0.1%
4.958589672 × 10181476
0.1%
4.95682899 × 10182566
0.2%
4.911818931 × 10189
 
< 0.1%
4.906628656 × 10182584
0.2%
4.897067971 × 10181038
0.1%
4.890424427 × 10181496
0.1%
Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.1 MiB
2025-12-02T08:18:00.619992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.132597
Min length13

Characters and Unicode

Total characters29995460
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Rippin, Kub and Mann
2nd rowfraud_Heller, Gutmann and Zieme
3rd rowfraud_Lind-Buckridge
4th rowfraud_Kutch, Hermiston and Farrell
5th rowfraud_Keeling-Crist
ValueCountFrequency (%)
and474111
 
15.7%
llc97780
 
3.2%
inc91939
 
3.0%
sons73145
 
2.4%
ltd70853
 
2.3%
plc66475
 
2.2%
group50447
 
1.7%
fraud_kutch10560
 
0.3%
fraud_schaefer9394
 
0.3%
fraud_streich9250
 
0.3%
Other values (804)2069403
68.4%
2025-12-02T08:18:00.968249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a2910697
 
9.7%
r2695758
 
9.0%
d2139780
 
7.1%
e1865710
 
6.2%
u1857912
 
6.2%
n1768848
 
5.9%
1726682
 
5.8%
f1397378
 
4.7%
_1296675
 
4.3%
o1129340
 
3.8%
Other values (45)11206680
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)29995460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2910697
 
9.7%
r2695758
 
9.0%
d2139780
 
7.1%
e1865710
 
6.2%
u1857912
 
6.2%
n1768848
 
5.9%
1726682
 
5.8%
f1397378
 
4.7%
_1296675
 
4.3%
o1129340
 
3.8%
Other values (45)11206680
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29995460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2910697
 
9.7%
r2695758
 
9.0%
d2139780
 
7.1%
e1865710
 
6.2%
u1857912
 
6.2%
n1768848
 
5.9%
1726682
 
5.8%
f1397378
 
4.7%
_1296675
 
4.3%
o1129340
 
3.8%
Other values (45)11206680
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29995460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2910697
 
9.7%
r2695758
 
9.0%
d2139780
 
7.1%
e1865710
 
6.2%
u1857912
 
6.2%
n1768848
 
5.9%
1726682
 
5.8%
f1397378
 
4.7%
_1296675
 
4.3%
o1129340
 
3.8%
Other values (45)11206680
37.4%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.5 MiB
gas_transport
131659 
grocery_pos
123638 
home
123115 
shopping_pos
116672 
kids_pets
113035 
Other values (9)
688556 

Length

Max length14
Median length12
Mean length10.526079
Min length4

Characters and Unicode

Total characters13648903
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmisc_net
2nd rowgrocery_pos
3rd rowentertainment
4th rowgas_transport
5th rowmisc_pos

Common Values

ValueCountFrequency (%)
gas_transport131659
10.2%
grocery_pos123638
9.5%
home123115
9.5%
shopping_pos116672
9.0%
kids_pets113035
8.7%
shopping_net97543
7.5%
entertainment94014
7.3%
food_dining91461
 
7.1%
personal_care90758
 
7.0%
health_fitness85879
 
6.6%
Other values (4)228901
17.7%

Length

2025-12-02T08:18:01.060507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas_transport131659
10.2%
grocery_pos123638
9.5%
home123115
9.5%
shopping_pos116672
9.0%
kids_pets113035
8.7%
shopping_net97543
7.5%
entertainment94014
7.3%
food_dining91461
 
7.1%
personal_care90758
 
7.0%
health_fitness85879
 
6.6%
Other values (4)228901
17.7%

Most occurring characters

ValueCountFrequency (%)
s1429026
10.5%
e1287345
9.4%
o1231724
9.0%
n1193757
8.7%
p1083847
 
7.9%
t1076942
 
7.9%
_1039039
 
7.6%
r917535
 
6.7%
i833007
 
6.1%
a665234
 
4.9%
Other values (10)2891447
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)13648903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s1429026
10.5%
e1287345
9.4%
o1231724
9.0%
n1193757
8.7%
p1083847
 
7.9%
t1076942
 
7.9%
_1039039
 
7.6%
r917535
 
6.7%
i833007
 
6.1%
a665234
 
4.9%
Other values (10)2891447
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13648903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s1429026
10.5%
e1287345
9.4%
o1231724
9.0%
n1193757
8.7%
p1083847
 
7.9%
t1076942
 
7.9%
_1039039
 
7.6%
r917535
 
6.7%
i833007
 
6.1%
a665234
 
4.9%
Other values (10)2891447
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13648903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s1429026
10.5%
e1287345
9.4%
o1231724
9.0%
n1193757
8.7%
p1083847
 
7.9%
t1076942
 
7.9%
_1039039
 
7.6%
r917535
 
6.7%
i833007
 
6.1%
a665234
 
4.9%
Other values (10)2891447
21.2%

amt
Real number (ℝ)

Skewed 

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-12-02T08:18:01.143490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.31604
Coefficient of variation (CV)2.2788014
Kurtosis4545.645
Mean70.351035
Median Absolute Deviation (MAD)37.5
Skewness42.277874
Sum91222429
Variance25701.232
MonotonicityNot monotonic
2025-12-02T08:18:01.261128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14542
 
< 0.1%
1.04538
 
< 0.1%
1.25535
 
< 0.1%
1.02533
 
< 0.1%
1.01523
 
< 0.1%
1.05519
 
< 0.1%
1.2516
 
< 0.1%
1.23515
 
< 0.1%
1.08512
 
< 0.1%
1.11509
 
< 0.1%
Other values (52918)1291433
99.6%
ValueCountFrequency (%)
1222
< 0.1%
1.01523
< 0.1%
1.02533
< 0.1%
1.03499
< 0.1%
1.04538
< 0.1%
1.05519
< 0.1%
1.06471
< 0.1%
1.07498
< 0.1%
1.08512
< 0.1%
1.09496
< 0.1%
ValueCountFrequency (%)
28948.91
< 0.1%
27390.121
< 0.1%
27119.771
< 0.1%
26544.121
< 0.1%
25086.941
< 0.1%
17897.241
< 0.1%
15305.951
< 0.1%
15047.031
< 0.1%
15034.181
< 0.1%
14849.741
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 MiB
F
709863 
M
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Length

2025-12-02T08:18:01.435661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T08:18:01.529307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f709863
54.7%
m586812
45.3%

Most occurring characters

ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

lat
Real number (ℝ)

High correlation 

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537622
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-12-02T08:18:01.632257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6205
median39.3543
Q341.9404
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.3199

Descriptive statistics

Standard deviation5.0758084
Coefficient of variation (CV)0.13171047
Kurtosis0.81296795
Mean38.537622
Median Absolute Deviation (MAD)3.3597
Skewness-0.18602768
Sum49970771
Variance25.763831
MonotonicityNot monotonic
2025-12-02T08:18:01.735490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.3853646
 
0.3%
26.11843613
 
0.3%
42.51643597
 
0.3%
43.00483527
 
0.3%
44.59953123
 
0.2%
39.89363123
 
0.2%
33.28873119
 
0.2%
34.03263117
 
0.2%
33.47833113
 
0.2%
44.33463112
 
0.2%
Other values (958)1263585
97.4%
ValueCountFrequency (%)
20.02711527
0.1%
20.08271032
 
0.1%
24.65572584
0.2%
26.11843613
0.3%
26.3304542
 
< 0.1%
26.3771518
 
< 0.1%
26.42153038
0.2%
26.47222524
0.2%
26.5291549
0.1%
26.69391027
 
0.1%
ValueCountFrequency (%)
66.693312
 
< 0.1%
65.6899540
 
< 0.1%
64.75561568
0.1%
48.88783030
0.2%
48.88562066
0.2%
48.83281533
0.1%
48.66691047
 
0.1%
48.60312973
0.2%
48.47862038
0.2%
48.343088
0.2%

long
Real number (ℝ)

High correlation 

Distinct969
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226335
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2025-12-02T08:18:01.829663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.0825
Q1-96.798
median-87.4769
Q3-80.158
95-th percentile-73.5112
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.64

Descriptive statistics

Standard deviation13.759077
Coefficient of variation (CV)-0.15249513
Kurtosis1.8558923
Mean-90.226335
Median Absolute Deviation (MAD)8.1527
Skewness-1.1501077
Sum-1.1699423 × 108
Variance189.3122
MonotonicityNot monotonic
2025-12-02T08:18:01.932866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98.07273646
 
0.3%
-81.73613613
 
0.3%
-82.98323597
 
0.3%
-108.89643527
 
0.3%
-79.78563123
 
0.2%
-86.21413123
 
0.2%
-111.09853119
 
0.2%
-82.20273117
 
0.2%
-90.51423113
 
0.2%
-73.0983112
 
0.2%
Other values (959)1263585
97.4%
ValueCountFrequency (%)
-165.67231568
0.1%
-156.292540
 
< 0.1%
-155.4881032
0.1%
-155.36971527
0.1%
-153.99412
 
< 0.1%
-124.44091043
0.1%
-124.21741547
0.1%
-124.15871031
0.1%
-124.14371526
0.1%
-123.97432036
0.2%
ValueCountFrequency (%)
-67.95032080
0.2%
-68.55651014
 
0.1%
-69.2675519
 
< 0.1%
-69.48282050
0.2%
-69.9576537
 
< 0.1%
-69.96563107
0.2%
-70.10319
 
< 0.1%
-70.2391036
 
0.1%
-70.30012090
0.2%
-70.34571527
0.1%

city_pop
Real number (ℝ)

Distinct879
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88824.441
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-12-02T08:18:02.037728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1743
median2456
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19585

Descriptive statistics

Standard deviation301956.36
Coefficient of variation (CV)3.3994738
Kurtosis37.614519
Mean88824.441
Median Absolute Deviation (MAD)2198
Skewness5.5938531
Sum1.1517643 × 1011
Variance9.1177644 × 1010
MonotonicityNot monotonic
2025-12-02T08:18:02.157908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6065496
 
0.4%
15957975130
 
0.4%
13129225075
 
0.4%
17664574
 
0.4%
2414533
 
0.3%
29067004168
 
0.3%
2760024155
 
0.3%
3024147
 
0.3%
9101484073
 
0.3%
1984067
 
0.3%
Other values (869)1251257
96.5%
ValueCountFrequency (%)
232049
0.2%
371013
 
0.1%
432034
0.2%
463040
0.2%
47511
 
< 0.1%
491054
 
0.1%
511016
 
0.1%
52518
 
< 0.1%
532610
0.2%
601045
 
0.1%
ValueCountFrequency (%)
29067004168
0.3%
25047002033
 
0.2%
2383912521
 
< 0.1%
15957975130
0.4%
15773852563
0.2%
15262063517
0.3%
14177938
 
< 0.1%
13824802056
0.2%
13129225075
0.4%
12633213629
0.3%

job
Text

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.5 MiB
2025-12-02T08:18:02.312940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.227102
Min length3

Characters and Unicode

Total characters26227978
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, counselling
2nd rowSpecial educational needs teacher
3rd rowNature conservation officer
4th rowPatent attorney
5th rowDance movement psychotherapist
ValueCountFrequency (%)
engineer131756
 
4.6%
officer110915
 
3.9%
manager61124
 
2.1%
scientist55878
 
1.9%
designer52218
 
1.8%
surveyor49062
 
1.7%
teacher38126
 
1.3%
psychologist32600
 
1.1%
research29754
 
1.0%
editor28725
 
1.0%
Other values (456)2289024
79.5%
2025-12-02T08:18:02.672059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e2803032
 
10.7%
i2386346
 
9.1%
r2198669
 
8.4%
a1813638
 
6.9%
t1782302
 
6.8%
n1764769
 
6.7%
1582507
 
6.0%
o1491775
 
5.7%
s1444701
 
5.5%
c1323152
 
5.0%
Other values (43)7637087
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)26227978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2803032
 
10.7%
i2386346
 
9.1%
r2198669
 
8.4%
a1813638
 
6.9%
t1782302
 
6.8%
n1764769
 
6.7%
1582507
 
6.0%
o1491775
 
5.7%
s1444701
 
5.5%
c1323152
 
5.0%
Other values (43)7637087
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26227978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2803032
 
10.7%
i2386346
 
9.1%
r2198669
 
8.4%
a1813638
 
6.9%
t1782302
 
6.8%
n1764769
 
6.7%
1582507
 
6.0%
o1491775
 
5.7%
s1444701
 
5.5%
c1323152
 
5.0%
Other values (43)7637087
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26227978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2803032
 
10.7%
i2386346
 
9.1%
r2198669
 
8.4%
a1813638
 
6.9%
t1782302
 
6.8%
n1764769
 
6.7%
1582507
 
6.0%
o1491775
 
5.7%
s1444701
 
5.5%
c1323152
 
5.0%
Other values (43)7637087
29.1%

merch_lat
Real number (ℝ)

High correlation 

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537338
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-12-02T08:18:02.777070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.751653
Q134.733572
median39.36568
Q341.957164
95-th percentile46.00353
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.1097884
Coefficient of variation (CV)0.13259318
Kurtosis0.79599391
Mean38.537338
Median Absolute Deviation (MAD)3.397536
Skewness-0.18191543
Sum49970403
Variance26.109937
MonotonicityNot monotonic
2025-12-02T08:18:02.941931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.9831384
 
< 0.1%
41.7316634
 
< 0.1%
34.1349944
 
< 0.1%
40.4563054
 
< 0.1%
42.9205844
 
< 0.1%
42.8893544
 
< 0.1%
40.5570264
 
< 0.1%
37.6957154
 
< 0.1%
43.3274954
 
< 0.1%
41.2714684
 
< 0.1%
Other values (1247795)1296635
> 99.9%
ValueCountFrequency (%)
19.0277851
< 0.1%
19.0278041
< 0.1%
19.0297981
< 0.1%
19.0312421
< 0.1%
19.0322771
< 0.1%
19.0332881
< 0.1%
19.0342821
< 0.1%
19.0346871
< 0.1%
19.0354721
< 0.1%
19.0363121
< 0.1%
ValueCountFrequency (%)
67.5102671
< 0.1%
67.4415181
< 0.1%
67.3970181
< 0.1%
67.1881111
< 0.1%
67.0642771
< 0.1%
66.8351741
< 0.1%
66.6829051
< 0.1%
66.673551
< 0.1%
66.6646731
< 0.1%
66.6592421
< 0.1%

merch_long
Real number (ℝ)

High correlation 

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226465
Minimum-166.67124
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2025-12-02T08:18:03.121125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.67124
5-th percentile-119.33009
Q1-96.897276
median-87.438392
Q3-80.236796
95-th percentile-73.354218
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.771091
Coefficient of variation (CV)-0.15262806
Kurtosis1.8484792
Mean-90.226465
Median Absolute Deviation (MAD)8.227889
Skewness-1.1469599
Sum-1.169944 × 108
Variance189.64294
MonotonicityNot monotonic
2025-12-02T08:18:03.584722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.6182694
 
< 0.1%
-81.2191894
 
< 0.1%
-87.1164144
 
< 0.1%
-88.493093
 
< 0.1%
-82.0550363
 
< 0.1%
-95.739373
 
< 0.1%
-94.0248183
 
< 0.1%
-79.5881553
 
< 0.1%
-123.1548623
 
< 0.1%
-82.6582243
 
< 0.1%
Other values (1275735)1296642
> 99.9%
ValueCountFrequency (%)
-166.6712421
< 0.1%
-166.6701321
< 0.1%
-166.6696381
< 0.1%
-166.6661791
< 0.1%
-166.6648281
< 0.1%
-166.6628881
< 0.1%
-166.6619681
< 0.1%
-166.6592771
< 0.1%
-166.6578341
< 0.1%
-166.6571741
< 0.1%
ValueCountFrequency (%)
-66.9509021
< 0.1%
-66.9559961
< 0.1%
-66.956541
< 0.1%
-66.9586591
< 0.1%
-66.9587511
< 0.1%
-66.9591781
< 0.1%
-66.9619231
< 0.1%
-66.9629131
< 0.1%
-66.9639181
< 0.1%
-66.9639751
< 0.1%

is_fraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Length

2025-12-02T08:18:03.695803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T08:18:03.767991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.804858
Minimum0
Maximum23
Zeros42502
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2025-12-02T08:18:03.850685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8178239
Coefficient of variation (CV)0.53244042
Kurtosis-1.0795803
Mean12.804858
Median Absolute Deviation (MAD)5
Skewness-0.28282545
Sum16603739
Variance46.482723
MonotonicityNot monotonic
2025-12-02T08:18:03.959257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2367104
 
5.2%
2266982
 
5.2%
1866051
 
5.1%
1665726
 
5.1%
2165533
 
5.1%
1965508
 
5.1%
1765450
 
5.0%
1565391
 
5.0%
1365314
 
5.0%
1265257
 
5.0%
Other values (14)638359
49.2%
ValueCountFrequency (%)
042502
3.3%
142869
3.3%
242656
3.3%
342769
3.3%
441863
3.2%
542171
3.3%
642300
3.3%
742203
3.3%
842505
3.3%
942185
3.3%
ValueCountFrequency (%)
2367104
5.2%
2266982
5.2%
2165533
5.1%
2065098
5.0%
1965508
5.1%
1866051
5.1%
1765450
5.0%
1665726
5.1%
1565391
5.0%
1464885
5.0%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.587978
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2025-12-02T08:18:04.039563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8291214
Coefficient of variation (CV)0.5664058
Kurtosis-1.1871417
Mean15.587978
Median Absolute Deviation (MAD)8
Skewness0.030847364
Sum20212542
Variance77.953384
MonotonicityNot monotonic
2025-12-02T08:18:04.118035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
147089
 
3.6%
1546213
 
3.6%
846201
 
3.6%
1644894
 
3.5%
244748
 
3.5%
944685
 
3.4%
744239
 
3.4%
1444015
 
3.4%
2843470
 
3.4%
1742272
 
3.3%
Other values (21)848849
65.5%
ValueCountFrequency (%)
147089
3.6%
244748
3.5%
341842
3.2%
441479
3.2%
541886
3.2%
641420
3.2%
744239
3.4%
846201
3.6%
944685
3.4%
1041934
3.2%
ValueCountFrequency (%)
3124701
1.9%
3041019
3.2%
2939617
3.1%
2843470
3.4%
2739684
3.1%
2640692
3.1%
2540374
3.1%
2441360
3.2%
2340815
3.1%
2242061
3.2%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1421497
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2025-12-02T08:18:04.219461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4177033
Coefficient of variation (CV)0.55643439
Kurtosis-1.0475463
Mean6.1421497
Median Absolute Deviation (MAD)3
Skewness0.29851575
Sum7964372
Variance11.680696
MonotonicityNot monotonic
2025-12-02T08:18:04.295731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5146875
11.3%
6143811
11.1%
3143789
11.1%
12141060
10.9%
4134970
10.4%
1104727
8.1%
297657
7.5%
887359
6.7%
786596
6.7%
970652
5.4%
Other values (2)139179
10.7%
ValueCountFrequency (%)
1104727
8.1%
297657
7.5%
3143789
11.1%
4134970
10.4%
5146875
11.3%
6143811
11.1%
786596
6.7%
887359
6.7%
970652
5.4%
1068758
5.3%
ValueCountFrequency (%)
12141060
10.9%
1170421
5.4%
1068758
5.3%
970652
5.4%
887359
6.7%
786596
6.7%
6143811
11.1%
5146875
11.3%
4134970
10.4%
3143789
11.1%

year
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.4 MiB
2019
924850 
2020
371825 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5186700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019924850
71.3%
2020371825
28.7%

Length

2025-12-02T08:18:04.391522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T08:18:04.460588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2019924850
71.3%
2020371825
28.7%

Most occurring characters

ValueCountFrequency (%)
21668500
32.2%
01668500
32.2%
1924850
17.8%
9924850
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5186700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21668500
32.2%
01668500
32.2%
1924850
17.8%
9924850
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5186700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21668500
32.2%
01668500
32.2%
1924850
17.8%
9924850
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5186700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21668500
32.2%
01668500
32.2%
1924850
17.8%
9924850
17.8%

dayofweek
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0706037
Minimum0
Maximum6
Zeros254282
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2025-12-02T08:18:04.548137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1981526
Coefficient of variation (CV)0.71586984
Kurtosis-1.445049
Mean3.0706037
Median Absolute Deviation (MAD)2
Skewness-0.078453041
Sum3981575
Variance4.8318747
MonotonicityNot monotonic
2025-12-02T08:18:04.636197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0254282
19.6%
6250579
19.3%
5200957
15.5%
1160227
12.4%
4152272
11.7%
3147285
11.4%
2131073
10.1%
ValueCountFrequency (%)
0254282
19.6%
1160227
12.4%
2131073
10.1%
3147285
11.4%
4152272
11.7%
5200957
15.5%
6250579
19.3%
ValueCountFrequency (%)
6250579
19.3%
5200957
15.5%
4152272
11.7%
3147285
11.4%
2131073
10.1%
1160227
12.4%
0254282
19.6%

card_holder_age
Real number (ℝ)

Distinct83
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.029298
Minimum14
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2025-12-02T08:18:04.740995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile22
Q133
median44
Q357
95-th percentile80
Maximum96
Range82
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.382373
Coefficient of variation (CV)0.37763714
Kurtosis-0.17600385
Mean46.029298
Median Absolute Deviation (MAD)12
Skewness0.61226204
Sum59685040
Variance302.14688
MonotonicityNot monotonic
2025-12-02T08:18:04.892479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4741337
 
3.2%
3539331
 
3.0%
3435816
 
2.8%
3235588
 
2.7%
3333430
 
2.6%
4533098
 
2.6%
4832719
 
2.5%
4632212
 
2.5%
4431035
 
2.4%
4330528
 
2.4%
Other values (73)951581
73.4%
ValueCountFrequency (%)
141318
 
0.1%
155817
 
0.4%
165104
 
0.4%
171191
 
0.1%
183901
 
0.3%
198203
 
0.6%
2016326
1.3%
2114915
1.2%
2224536
1.9%
2313209
1.0%
ValueCountFrequency (%)
96138
 
< 0.1%
95398
 
< 0.1%
941722
 
0.1%
935684
0.4%
924450
0.3%
914824
0.4%
905443
0.4%
893916
0.3%
883843
0.3%
872364
0.2%

Interactions

2025-12-02T08:17:49.955835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:12.328942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:15.806226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:19.062355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:22.399650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:25.720846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:28.939161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:32.619350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:35.798306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:39.009895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:42.853317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:46.564464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:50.292237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:12.611405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:16.201520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:19.330161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:22.682158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:25.983798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:29.197595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:32.883623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:36.048502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:39.307150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:43.191120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:46.833193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:50.754917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:12.876213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:16.439886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:19.624827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:22.949276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:26.252432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:29.499899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:33.137556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:36.314240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:39.622674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:43.557166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:47.108861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:51.153228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:13.135803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:16.685398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:19.913807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:23.197517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:26.512588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:29.823356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:33.398141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:36.621702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:39.922578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:43.887520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:47.361928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:51.590069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:13.392325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:16.963822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:20.178885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:23.486145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:26.760894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:30.089382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:33.654386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:36.864134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:40.188505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:44.250623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:47.635770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:51.955216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:13.671138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:17.244816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:20.490794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:23.759692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:27.008985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:30.385403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:33.916224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:37.186279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:40.554435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:44.539253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:47.960535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:52.251535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:13.950322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:17.485513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:20.748772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:24.041125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:27.257401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:30.752115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:34.182442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:37.430266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:40.878835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:44.785494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:48.265461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:52.536872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:14.188486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:17.746912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:20.996817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:24.304798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:27.515836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:31.035600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:34.431971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:37.662933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:41.210863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:45.047951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:48.519741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:53.119553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:14.460621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:18.013457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:21.255411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:24.596041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:27.795525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:31.305359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:34.698335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:37.958192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:41.473078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:45.297626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:48.787387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:53.447956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:14.732643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:18.273897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:21.555263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:24.912782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:28.087971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:31.552412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:35.001566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:38.232161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:41.754932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:45.610723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:49.069505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:53.901001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:15.072184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:18.552660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:21.829860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:25.177364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:28.362454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:31.842482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:35.240570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:38.504799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:42.077311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:45.994784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:49.329964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:54.290195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:15.434791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:18.807433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:22.111350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:25.425439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:28.641177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:32.360449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:35.504848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:38.754795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:42.431771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:46.305666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T08:17:49.608918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-02T08:18:05.010898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amtcard_holder_agecategorycc_numcity_popdaydayofweekgenderhouris_fraudlatlongmerch_latmerch_longmonthyear
amt1.000-0.0240.020-0.001-0.0240.000-0.0010.000-0.1540.0000.012-0.0000.0120.000-0.0030.000
card_holder_age-0.0241.0000.046-0.037-0.157-0.001-0.0140.110-0.1730.0180.037-0.0200.036-0.020-0.0100.041
category0.0200.0461.0000.0090.0140.0010.0030.0540.2710.0710.0110.0090.0110.0090.0010.000
cc_num-0.001-0.0370.0091.0000.049-0.000-0.0010.0510.0110.006-0.004-0.013-0.004-0.0130.0010.000
city_pop-0.024-0.1570.0140.0491.000-0.0010.0020.0890.0330.004-0.2650.087-0.2640.0860.0010.001
day0.000-0.0010.001-0.000-0.0011.0000.0170.000-0.0000.009-0.0000.000-0.0000.0000.0080.057
dayofweek-0.001-0.0140.003-0.0010.0020.0171.0000.0060.0000.0120.0010.0010.0000.0010.0380.090
gender0.0000.1100.0540.0510.0890.0000.0061.0000.0450.0080.1010.0910.1030.0820.0020.000
hour-0.154-0.1730.2710.0110.033-0.0000.0000.0451.0000.095-0.011-0.006-0.010-0.006-0.0010.001
is_fraud0.0000.0180.0710.0060.0040.0090.0120.0080.0951.0000.0080.0060.0080.0050.0180.003
lat0.0120.0370.011-0.004-0.265-0.0000.0010.101-0.0110.0081.0000.1060.9910.105-0.0010.002
long-0.000-0.0200.009-0.0130.0870.0000.0010.091-0.0060.0060.1061.0000.1060.998-0.0010.000
merch_lat0.0120.0360.011-0.004-0.264-0.0000.0000.103-0.0100.0080.9910.1061.0000.104-0.0010.000
merch_long0.000-0.0200.009-0.0130.0860.0000.0010.082-0.0060.0050.1050.9980.1041.000-0.0010.000
month-0.003-0.0100.0010.0010.0010.0080.0380.002-0.0010.018-0.001-0.001-0.001-0.0011.0000.527
year0.0000.0410.0000.0000.0010.0570.0900.0000.0010.0030.0020.0000.0000.0000.5271.000

Missing values

2025-12-02T08:17:54.602140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-02T08:17:56.160457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trans_date_trans_timecc_nummerchantcategoryamtgenderlatlongcity_popjobmerch_latmerch_longis_fraudhourdaymonthyeardayofweekcard_holder_age
02019-01-01 00:00:182703186189652095fraud_Rippin, Kub and Mannmisc_net4.97F36.0788-81.17813495Psychologist, counselling36.011293-82.04831500112019131
12019-01-01 00:00:44630423337322fraud_Heller, Gutmann and Ziemegrocery_pos107.23F48.8878-118.2105149Special educational needs teacher49.159047-118.18646200112019141
22019-01-01 00:00:5138859492057661fraud_Lind-Buckridgeentertainment220.11M42.1808-112.26204154Nature conservation officer43.150704-112.15448100112019157
32019-01-01 00:01:163534093764340240fraud_Kutch, Hermiston and Farrellgas_transport45.00M46.2306-112.11381939Patent attorney47.034331-112.56107100112019152
42019-01-01 00:03:06375534208663984fraud_Keeling-Cristmisc_pos41.96M38.4207-79.462999Dance movement psychotherapist38.674999-78.63245900112019133
52019-01-01 00:04:084767265376804500fraud_Stroman, Hudson and Erdmangas_transport94.63F40.3750-75.20452158Transport planner40.653382-76.15266700112019158
62019-01-01 00:04:4230074693890476fraud_Rowe-Vandervortgrocery_net44.54F37.9931-100.98932691Arboriculturist37.162705-100.15337000112019126
72019-01-01 00:05:086011360759745864fraud_Corwin-Collinsgas_transport71.65M38.8432-78.60036018Designer, multimedia38.948089-78.54029600112019172
82019-01-01 00:05:184922710831011201fraud_Herzog Ltdmisc_pos4.27F40.3359-79.66071472Public affairs consultant40.351813-79.95814600112019178
92019-01-01 00:06:012720830304681674fraud_Schoen, Kuphal and Nitzschegrocery_pos198.39F36.5220-87.3490151785Pathologist37.179198-87.48538100112019145
trans_date_trans_timecc_nummerchantcategoryamtgenderlatlongcity_popjobmerch_latmerch_longis_fraudhourdaymonthyeardayofweekcard_holder_age
12966652020-06-21 12:08:42213193596103206fraud_Gulgowski LLChome72.17M45.7549-84.447095Electrical engineer44.938461-83.9962340122162020626
12966662020-06-21 12:09:224587657402165341815fraud_Hyatt, Russel and Gleichnerhealth_fitness7.30F41.0646-87.59172135Psychotherapist, child40.556811-88.0923390122162020616
12966672020-06-21 12:10:564822367783500458fraud_Hahn, Douglas and Schowaltertravel19.71M28.0758-81.592933804Exercise physiologist27.465871-81.5118040122162020629
12966682020-06-21 12:11:23213141712584544fraud_Metz, Russel and Metzkids_pets100.85F32.1530-90.121719685Fine artist31.377697-90.5284500122162020636
12966692020-06-21 12:11:364400011257587661852fraud_Stiedemann Incmisc_pos37.38F41.4972-98.7858509Nurse, children's41.728638-99.0396600122162020640
12966702020-06-21 12:12:0830263540414123fraud_Reichel Incentertainment15.56M37.7175-112.4777258Geoscientist36.841266-111.6907650122162020659
12966712020-06-21 12:12:196011149206456997fraud_Abernathy and Sonsfood_dining51.70M39.2667-77.5101100Production assistant, television38.906881-78.2465280122162020641
12966722020-06-21 12:12:323514865930894695fraud_Stiedemann Ltdfood_dining105.93M32.9396-105.8189899Naval architect33.619513-105.1305290122162020653
12966732020-06-21 12:13:362720012583106919fraud_Reinger, Weissnat and Strosinfood_dining74.90M43.3526-102.54111126Volunteer coordinator42.788940-103.2411600122162020640
12966742020-06-21 12:13:374292902571056973207fraud_Langosh, Wintheiser and Hyattfood_dining4.30M45.8433-113.8748218Therapist, horticultural46.565983-114.1861100122162020625